1. Field of the Invention
The invention relates to a method and system for capturing signals and to associated signal processing techniques. This invention further relates to a method and system for hands free operation of mobile or non-mobile phones.
2. Background Art
In any system for capturing signals, the goal is to capture the desired signal while rejecting undesired signals. Signal processing techniques are employed to process a received input signal to enhance the desired signal while removing the undesired signals.
One particular problem faced in systems for hands free operation of mobile or non-mobile phones is the acoustic echo cancellation (AEC) problem. The AEC problem is a well known problem, and it can be described as shown in FIG. 1, where the far-end received signal (x(n)) is sent to a loud speaker inside of a car (for example). This signal is propagated by the interior of the automobile through the acoustic path (q(n)), and is fed back into the microphone generating the echo signal (c(n)). To cancel the echo signal an adaptive filter is used, where the objective is to identify the acoustic echo path (q(n)) with the adaptive filter (g(n)), and then to subtract the resultant signal (y(n)) from the microphone signal. If (g(n)=q(n)) then (y(n)=c(n)), and the subtraction of the output signal of the adaptive filter from the microphone signal will cancel the echo signal.
This AEC problem has been addressed in existing applications by using different types of adaptive filter algorithms such as least mean square algorithm (LMS), normalized least mean square algorithm (NLMS), data reuse normalized least mean square algorithm (DRNLMS), recursive least square algorithm (RLS), affine projection algorithm (APA), and others.
Another related problem is that an adaptive filter algorithm needs some type of control to prevent the divergence of the algorithm when far-end send and near-end receive signals are present at the same time.
This divergence problem has been addressed in existing applications by introducing a double talk detector (DTD). The DTD restricts the conditions under which the adaptive filter algorithm may adapt.
One particular requirement of any system is that the system must perform well in the presence of a noise signal (v(n)). In attempts to meet this requirement, a noise cancellation algorithm (NC) has been introduced. Various different approaches have been taken for implementing the NC algorithm including approaches based on spectral subtraction, Kalman filters, neural networks, and others.
In another aspect, existing applications have introduced a nonlinear processor (NLP). The NLP attempts to compensate for the practical problem of the adaptive filter algorithm not achieving its minimum mean square error (MSE) and for system nonlinearity particularly where one of the sources is the nonlinear loud speaker.
Overall, existing applications have taken a variety of approaches to address acoustic echo, adaptive algorithm divergence, noise, and system nonlinearity. The initial problem of acoustic echo cancellation has developed into an evolving complex problem involving a number of different design aspects. Although various approaches have been taken in addressing specific issues, the overall evolving complex problem has yet to be fully addressed.
Background information may be found in S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, N.J., 4th Edition, 2002; P. S. R. Diniz, Adaptive Filtering—Algorithms and Practical Implementation, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2nd Edition, 2002; P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall Signal Processing Series, Englewood Cliffs, N.J., 1993; R. E. Crochiere, L. R. Rabiner, Multirate Digital Signal Processing, Prentice Hall, Englewood Cliffs, N.J.; S. T. Gay, J. Benesty, Acoustic Signal Processing for Telecommunication, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000; S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoust., Speech, Signal Proc., vol. ASSP-27, Apr. 1979; R. B. Jeannes, P. Scalart, G. Faucon, C. Beaugeant, “Combined noise and echo reduction in hands free systems: A survey,” IEEE Trans. Speech Audio Processing, vol. 9, pp 808-820, November 2001; R. Martin, J. Altenhoner, “Coupled Adaptive Filters for Acoustic Echo Control and Noise Reduction,” Proc. ICASSP 95, pp. 3043-3046, May 1995; M. R. Petraglia, R. G. Alves, P. S. R. Diniz, “New Structures for Adaptive Filtering in Subbands with Critical Sampling,” IEEE Transactions on Signal Processing, Vol. 48, No. 12, December 2000; M. R. Petraglia, R. G. Alves, P. S. R. Diniz, “Convergence Analysis of an Oversampled Subband Adaptive Filtering Structure with Local Errors,” Proc. IEEE Int. Symp. on Circuits and Systems (ISCAS), May 2000.
Further, background information may be found in A. Stenger and W. Kellermann, “Adaptation of a Memoryless Preprocessor for Nonlinear Acoustic Echo Cancelling,” In Signal Processing, vol. 80, pp. 1747-1760, Elsevier, September, 2000; J. Sjöberg and L. S. H. Ngia, Nonlinear Modeling, Advanced Black-Box Techniques, chapter “Some Aspects of Neural Nets and Related Model Structures for Nonlinear System Identification,” pages 1-28, Kluwer Academic Publisher, 1998; and J. E. Dennis and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, N.J., 1983.
For the foregoing reasons, there is a need for an improved method and system for clear signal capture that provides a practical solution to this evolving complex problem.